Ant colony optimisation of decision tree and contingency table models for the discovery of gene–gene interactions. Issue 6 (1st December 2015)

• Record Type: Journal Article
• Authors: Sapin, Emmanuel; Keedwell, Ed; Frayling, Tim
• Is Part Of: IET systems biology. Volume 9:Issue 6(2015)
• Page Start: 218
• Page End: 225
• Publication Date: 2015-12-01
• Subjects: genetics -- genomics -- DNA -- polymorphism -- molecular biophysics -- molecular configurations -- ant colony optimisation -- decision trees -- bioinformatics -- diseases; ant colony optimisation -- decision tree -- contingency table models -- gene‐gene interactions -- ACO algorithm -- near‐optimal interactions -- single nucleotide polymorphisms -- SNP -- genome‐wide association studies -- type II diabetes; Systems biology -- Periodicals; Cell physiology -- Periodicals; Biological systems -- Mathematical models -- Periodicals; Genetics -- Mathematical models -- Periodicals; Computational biology -- Periodicals; 573
• DOI: 10.1049/iet-syb.2015.0017
• Languages: English
• ISSNs: 1751-8849

Abstract

Abstract : In this study, ant colony optimisation (ACO) algorithm is used to derive near‐optimal interactions between a number of single nucleotide polymorphisms (SNPs). This approach is used to discover small numbers of SNPs that are combined into a decision tree or contingency table model. The ACO algorithm is shown to be very robust as it is proven to be able to find results that are discriminatory from a statistical perspective with logical interactions, decision tree and contingency table models for various numbers of SNPs considered in the interaction. A large number of the SNPs discovered here have been already identified in large genome‐wide association studies to be related to type II diabetes in the literature, lending additional confidence to the results.